Work

Improving Land Surface Temperature Estimation in Cloud Cover Scenarios Using Graph‐Based Propagation

Year: 2024

Type: article

Abstract: Land surface temperature (LST) serves as an important climate variable which is relevant to a number of studies related to energy and water exchanges, vegetation growth and urban heat island effects. Although LST can be derived from satellite observations, these approaches rely on cloud‐free acquisitions. This represents a significant obstacle in regions which are prone to cloud cover. In this paper, a graph‐based propagation method, referred to as GraphProp, is introduced. This method can accurately obtain LST values which would otherwise have been missing due to cloud cover. To validate this approach, a series of experiments are presented using synthetically obscured Landsat acquisitions. The validation takes place over scenarios ranging from between 10% and 90% cloud cover across six urban locations. In presented experiments, GraphProp recovers missing LST values with a mean absolute error of less than 1.1°C, 1.0°C and 1.8°C in 90% cloud cover scenarios across the studied locations respectively.

Source: Geophysical Research Letters

DOI: 10.1029/2024gl108263

Authors: Iain Rolland, Sivasakthy Selvakumaran, Shaikh Fairul Edros Ahmad Shaikh, Perrine Hamel, Andrea Marinoni

Institutions: University of Cambridge, Nanyang Technological University, UiT The Arctic University of Norway

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,900